Computer vision techniques are increasingly used to detect or classify objects in images. For example, in many surveillance applications, computer vision techniques are employed to identify certain objects, such as people and vehicles. In addition, many surveillance applications require that an identified object be tracked across an image sequence. While current computer vision techniques can effectively track one or more objects across a sequence of images from the same camera, existing technologies have been unable to reliably track an object of interest across image sequences from different cameras and viewpoints.
The recognition and measurement of properties of objects seen in images from different cameras and viewpoints is a challenging problem. Generally, different viewpoints can cause an object to appear to have different properties, such as size and speed, depending on their position in the image and the viewpoint characteristics. Existing solutions rely on known geometry and manual calibration procedures. A need exists for an automated procedure for normalizing image object data for measuring properties and performing classification.